System

Abstract

An object of a system according to an embodiment is to optimize an arrangement and a storage method of articles in a warehouse and realize an efficient operation.SOLUTION: A system according to an embodiment includes a learning unit, an optimization unit, and an arrangement generation unit. The learning unit learns how to arrange or store articles in a warehouse. The optimization unit optimizes the current arrangement of the ordered and shipped products and the warehouse based on the information learned by the learning unit. The layout generation unit generates a new layout when it is determined that rearrangement is necessary based on the information optimized by the optimization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has not adequately optimized the placement and storage methods of items within warehouses, making efficient operation difficult.

[0005] The system according to the embodiment aims to optimize the arrangement and storage method of items in a warehouse and achieve efficient operation. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, an optimization unit, and a layout generation unit. The learning unit learns how to arrange or store items in a warehouse. The optimization unit optimizes the current layout of ordered and shipped items and the warehouse based on the information learned by the learning unit. The layout generation unit generates a new layout plan based on the information optimized by the optimization unit when it is determined that rearrangement is necessary. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the arrangement and storage method of items in a warehouse, thereby achieving efficient operation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An AI system according to an embodiment of the present invention learns how items are arranged and stored in a warehouse, and optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. The AI ​​system learns how items are arranged and stored in a warehouse, and optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. If it determines that rearrangement is necessary, it generates a new layout diagram. For example, the AI ​​system collects data such as the type, quantity, and location of items in the warehouse, and a generation AI analyzes this data. Next, the AI ​​system optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. If it determines that rearrangement is necessary, the generation AI generates a new layout diagram. This allows the AI ​​system to optimize the shipping order and location of items in the warehouse, improving the efficiency of shipping operations. If rearrangement is necessary, the generation AI generates a new layout diagram, making it easier to manage items in the warehouse. This allows the AI ​​system to optimize the shipping order and location of items in the warehouse, improving the efficiency of shipping operations. For example, the time required for shipping operations is shortened, the burden on workers is reduced, and the risk of items being lost or shipped incorrectly is reduced.

[0029] The AI ​​system according to the embodiment includes a learning unit, an optimization unit, and an arrangement generation unit. The learning unit learns how to arrange and store items in a warehouse. For example, the learning unit collects data such as the type, quantity, and location of items in the warehouse, and the generation AI analyzes the data. The learning unit can also learn information such as how specific items are arranged on which shelves and which items are frequently shipped. Furthermore, the learning unit can use the generation AI to learn algorithms for optimizing how items are arranged and stored. For example, the generation AI learns optimal arrangements and storage methods based on past data. The optimization unit optimizes the current arrangement of ordered and shipped items and warehouse space based on the information learned by the learning unit. For example, the optimization unit arranges items that are frequently shipped in locations that make them easy to access. The optimization unit can also suggest optimal storage locations when ordered items arrive. Furthermore, the optimization unit can use the generation AI to execute algorithms for optimizing the current arrangement of ordered and shipped items and warehouse space. For example, the generation AI suggests optimal arrangements based on data on ordered and shipped items. The layout generation unit generates a new layout plan when it is determined that rearrangement is necessary based on the information optimized by the optimization unit. For example, the layout generation unit proposes an optimal layout for items that are in high demand during specific periods, such as seasonal products or campaign products. The layout generation unit can also rearrange items within a warehouse. Furthermore, the layout generation unit can execute an algorithm for generating a new layout plan using a generation AI. For example, the generation AI generates a new layout plan based on data on items that require rearrangement. As a result, the AI ​​system according to the embodiment can optimize the shipping order and positions of items within a warehouse and generate a new layout plan when rearrangement is necessary.

[0030] The AI ​​system includes a collection unit that collects data on the type or quantity of items in a warehouse and their locations. The collection unit collects data on the type, quantity, and locations of items in the warehouse. For example, the collection unit collects item data using barcode scanning. The collection unit can also collect item data automatically using sensors. Furthermore, the collection unit can manually input item data. For example, the collection unit provides an interface for a worker to manually input item data. In this way, the collection unit collects data on items in the warehouse, allowing the learning unit to learn based on more accurate information.

[0031] The optimization unit can place items that are shipped frequently in locations that are easy to retrieve. For example, the optimization unit identifies items that are shipped frequently based on past shipping history and places them in locations that are easy to retrieve. The optimization unit can also place items that are shipped frequently near the entrance of the warehouse. Furthermore, the optimization unit can place items that are shipped frequently on shelves that are easy to retrieve. For example, the optimization unit places items that are shipped frequently on shelves near the entrance of the warehouse, improving the efficiency of shipping operations. In this way, by placing items that are shipped frequently in locations that are easy to retrieve, the efficiency of shipping operations can be improved.

[0032] The optimization unit can propose the optimal storage location when the ordered items arrive. For example, when the ordered items arrive, the optimization unit proposes the optimal storage location based on the available space in the warehouse. The optimization unit can also propose the optimal storage location based on the type and size of the ordered items. Furthermore, the optimization unit can also propose the optimal storage location based on the shipping frequency of the ordered items. For example, if the shipping frequency of the ordered items is high, the optimization unit proposes storing the ordered items in a location that makes them easy to access. In this way, by proposing the optimal storage location when the ordered items arrive, it becomes easier to manage items in the warehouse.

[0033] The placement generation unit can propose an optimal placement for items that are in high demand during specific periods, such as seasonal products or campaign products. The placement generation unit proposes an optimal placement based on, for example, the sales period of seasonal products or campaign products. The placement generation unit can also propose an optimal placement based on the shipping history of items that are in high demand during specific periods. Furthermore, the placement generation unit can also propose an optimal placement based on the type and quantity of items that are in high demand during specific periods. For example, the placement generation unit proposes placing seasonal products in a location that makes them easy to access during their sales period. This allows for the efficient management of items in the warehouse by proposing an optimal placement for items that are in high demand during specific periods.

[0034] The placement generation unit can rearrange items in the warehouse. For example, the placement generation unit periodically reviews the placement of items in the warehouse and proposes an optimal placement. The placement generation unit can also rearrange items based on the shipping frequency and inventory status of the items. Furthermore, the placement generation unit can rearrange items in accordance with demand for seasonal products or campaign products. For example, the placement generation unit proposes rearranging items in the warehouse after the sales period for seasonal products has ended. In this way, rearranging items in the warehouse can improve the efficiency of item management.

[0035] During learning, the learning unit can optimize the learning algorithm by taking into account the deterioration status of items in the warehouse. For example, the learning unit has the generation AI monitor the deterioration status of items in real time and prioritize learning on items that are deteriorating. The learning unit can also have the generation AI predict the rate at which items will deteriorate and apply a special learning algorithm to items that deteriorate quickly. Furthermore, the learning unit can prioritize learning on items that are less deteriorated based on the deterioration status of the items and optimize long-term storage methods. For example, the learning unit evaluates the deterioration status based on the physical damage and expiration date of the items and optimizes the learning algorithm based on the results. In this way, by optimizing the learning algorithm by taking into account the deterioration status of the items, it is possible to prioritize managing items that are deteriorating.

[0036] During learning, the learning unit can refer to the shipping history of items to learn the optimal arrangement and storage methods. For example, the generation AI analyzes past shipping history and learns how to arrange frequently shipped items in locations that make them easy to access. The learning unit can also have the generation AI learn seasonal shipping patterns from the shipping history and suggest ways to efficiently arrange seasonal products. Furthermore, the learning unit can have the generation AI learn the optimal arrangement and storage methods for specific customers based on the shipping history and suggest customized arrangements. For example, the learning unit learns the optimal arrangement and storage methods based on past shipping data and shipping frequency. This makes it possible to improve the efficiency of shipping operations by learning the optimal arrangement and storage methods by referring to the shipping history of items.

[0037] During learning, the learning unit can learn the optimal arrangement method based on the size and weight of the items. For example, the learning unit allows the generation AI to learn how to arrange large and heavy items on the bottom shelf and small and light items on the top shelf, taking into account the size and weight of the items. The learning unit can also learn an arrangement method that will enable the generation AI to achieve efficient space utilization based on the size and weight of the items. Furthermore, the learning unit can also allow the generation AI to learn an arrangement method that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the learning unit learns the optimal arrangement method based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by learning the optimal arrangement method based on the size and weight of the items.

[0038] During learning, the learning unit can adjust the learning algorithm by taking into account environmental data such as temperature or humidity within the warehouse. For example, the generation AI of the learning unit learns how to arrange items that are resistant to temperature changes based on temperature data within the warehouse. The generation AI of the learning unit can also learn the optimal storage method for items that are sensitive to humidity by taking humidity data into account. Furthermore, the generation AI of the learning unit can combine temperature and humidity data to learn the optimal item arrangement method for environmental conditions. For example, the learning unit adjusts the learning algorithm based on temperature and humidity measurement data from sensors. In this way, the quality of items can be maintained by adjusting the learning algorithm by taking into account environmental data within the warehouse.

[0039] During learning, the learning unit can learn the optimal storage method by referring to information about the supplier of the item. For example, the learning unit uses the generation AI to learn the optimal storage method for each supplier based on information about the supplier of the item. The learning unit can also analyze the shipping patterns of the supplier and learn efficient storage methods. Furthermore, the learning unit can also propose the optimal storage method for items from a specific supplier by taking into account the characteristics of the supplier. For example, the learning unit learns the optimal storage method based on data such as the name, location, and supply history of the supplier. This makes it possible to learn the optimal storage method by referring to information about the supplier of the item.

[0040] During learning, the learning unit can learn the optimal placement method by taking into account the expiration date of the items. For example, the learning unit allows the generation AI to learn how to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the items. The learning unit can also allow the generation AI to learn how to efficiently store items with a long expiration date, based on the expiration date. Furthermore, the learning unit can also suggest the optimal placement method to prevent items from expiring, based on the expiration date. For example, the learning unit evaluates the expiration date based on the number of days elapsed since the date of manufacture and the results of quality inspections, and learns the optimal placement method based on the results. In this way, by learning the optimal placement method by taking into account the expiration date of the items, it is possible to prevent items from expiring.

[0041] During optimization, the optimization unit can determine the optimal placement based on the shipping frequency of the items. For example, the generation AI analyzes the shipping frequency of the items and places frequently shipped items in locations where they are easy to access. The optimization unit can also place items with less shipping frequency at the back of the warehouse to efficiently utilize space. Furthermore, the optimization unit can suggest a placement that maximizes the efficiency of shipping operations based on the shipping frequency of the generation AI. For example, the optimization unit calculates the shipping frequency based on past shipping history and determines the optimal placement based on the result. In this way, the efficiency of shipping operations can be improved by determining the optimal placement based on the shipping frequency of the items.

[0042] During optimization, the optimization unit can determine the optimal placement by taking into account the difficulty of handling the items. For example, the optimization unit uses the generation AI to evaluate the difficulty of handling the items and place items that are difficult to handle in locations where they are easy to retrieve. The optimization unit can also place items that are easy to handle at the back of the warehouse to efficiently use space. Furthermore, the optimization unit can suggest placements that reduce the burden on workers based on the handling difficulty of the generation AI. For example, the optimization unit evaluates the handling difficulty based on the weight, shape, and effort required to handle the items, and determines the optimal placement based on the results. In this way, the burden on workers can be reduced by determining the optimal placement by taking into account the difficulty of handling the items.

[0043] During optimization, the optimization unit can determine the optimal placement by taking into account the storage conditions (temperature, humidity) of the items. For example, the optimization unit places items that are sensitive to temperature and humidity in the optimal location based on the storage conditions of the items using the generation AI. The optimization unit can also appropriately place items that require specific environmental conditions by taking into account the storage conditions. Furthermore, the optimization unit can also suggest the optimal placement for maintaining the quality of the items based on the storage conditions using the generation AI. For example, the optimization unit evaluates storage conditions based on the appropriate temperature range and humidity control standards, and determines the optimal placement based on the results. In this way, the quality of the items can be maintained by determining the optimal placement by taking into account the storage conditions of the items.

[0044] During optimization, the optimization unit can determine the optimal placement by taking into account information about the supplier of the item. For example, the generation AI of the optimization unit determines the optimal placement for each supplier based on information about the supplier of the item. The optimization unit can also analyze the shipping patterns of the supplier and determine an efficient placement. Furthermore, the optimization unit can propose the optimal placement for items from a specific supplier by taking into account the characteristics of the supplier through the generation AI. For example, the optimization unit determines the optimal placement based on data such as the name, location, and supply history of the supplier. This enables efficient storage by determining the optimal placement by taking into account information about the supplier of the item.

[0045] During optimization, the optimization unit can determine the optimal placement by taking into account the expiration date of the items. For example, the optimization unit uses the generation AI to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the items. The optimization unit can also store items with a long expiration date efficiently, based on the expiration date of the items. Furthermore, the optimization unit can propose the optimal placement to prevent items from expiring, based on the expiration date of the items. For example, the optimization unit evaluates the expiration date based on the number of days elapsed since the date of manufacture and the results of quality inspections, and determines the optimal placement based on the results. In this way, by determining the optimal placement by taking into account the expiration date of the items, it is possible to prevent items from expiring.

[0046] During optimization, the optimization unit can determine the optimal layout by taking into account the size and weight of the items. For example, the optimization unit may have the generation AI consider the size and weight of the items and place large, heavy items on the bottom shelves and small, light items on the top shelves. The optimization unit may also determine a layout that will achieve efficient space utilization based on the size and weight of the items. Furthermore, the optimization unit may suggest a layout that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the optimization unit may determine the optimal layout based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by determining the optimal layout by taking into account the size and weight of the items.

[0047] When generating a layout diagram, the layout generation unit can refer to the shipping history of the items to generate an optimal layout diagram. For example, the generation AI analyzes past shipping history and generates a layout diagram in which frequently shipped items are placed in locations where they are easy to access. The layout generation unit can also learn seasonal shipping patterns from the shipping history and generate a layout diagram in which seasonal products are efficiently placed. Furthermore, the generation AI can generate a layout diagram that reflects the optimal arrangement and storage method for a specific customer based on the shipping history. For example, the layout generation unit generates an optimal layout diagram based on past shipping data and shipping frequency. In this way, the efficiency of shipping work can be improved by generating an optimal layout diagram by referring to the shipping history of the items.

[0048] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram based on the size and weight of the items. For example, the layout generation unit generates a layout diagram in which the generation AI takes into account the size and weight of the items and places large, heavy items on the lower shelves and small, light items on the upper shelves. The layout generation unit can also generate a layout diagram that achieves efficient space utilization based on the size and weight of the items. Furthermore, the layout generation unit can generate a layout diagram that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the layout generation unit generates an optimal layout diagram based on measurements of the dimensions and weight of the items. In this way, efficient space utilization can be achieved by generating an optimal layout diagram based on the size and weight of the items.

[0049] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the storage conditions (temperature, humidity) of the items. For example, the layout generation unit generates a layout diagram in which the generation AI places items that are sensitive to temperature and humidity in the optimal location based on the storage conditions of the items. The layout generation unit can also generate a layout diagram in which the generation AI takes storage conditions into account and appropriately places items that require specific environmental conditions. Furthermore, the layout generation unit can generate an optimal layout diagram for maintaining the quality of the items based on the storage conditions. For example, the layout generation unit evaluates storage conditions based on the appropriate temperature range and humidity control standards, and generates an optimal layout diagram based on the results. In this way, the quality of the items can be maintained by generating an optimal layout diagram by taking into account the storage conditions of the items.

[0050] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account information about the supplier of the item. For example, the generation AI of the layout generation unit generates an optimal layout diagram for each supplier based on information about the supplier of the item. The layout generation unit can also generate an efficient layout diagram by analyzing the shipping patterns of the supplier. Furthermore, the layout generation unit can generate an optimal layout diagram for items from a specific supplier by taking into account the characteristics of the supplier. For example, the layout generation unit generates an optimal layout diagram based on data such as the name, location, and supply history of the supplier. This enables efficient storage by generating an optimal layout diagram by taking into account information about the supplier of the item.

[0051] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the expiration date of the items. For example, the layout generation unit generates a layout diagram in which the generation AI places items with upcoming expiration dates in locations where they are easy to access, based on the expiration dates of the items. The layout generation unit can also generate a layout diagram in which the generation AI takes expiration dates into account and efficiently stores items with longer expiration dates. Furthermore, the layout generation unit can generate an optimal layout diagram to prevent expiration dates based on the expiration dates. For example, the layout generation unit evaluates expiration dates based on the number of days elapsed since the date of manufacture and quality inspection results, and generates an optimal layout diagram based on the results. In this way, expiration dates can be prevented by generating an optimal layout diagram by taking expiration dates into account.

[0052] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the difficulty of handling items. For example, the layout generation unit generates a layout diagram in which the generation AI evaluates the difficulty of handling items and places difficult-to-handle items in locations where they are easy to retrieve. The layout generation unit can also generate a layout diagram in which the generation AI places easy-to-handle items in the back of the warehouse to efficiently utilize space. Furthermore, the layout generation unit can generate a layout diagram that reduces the burden on workers based on the handling difficulty. For example, the layout generation unit evaluates the handling difficulty based on the weight, shape, and handling effort of the item, and generates an optimal layout diagram based on the results. In this way, the burden on workers can be reduced by generating an optimal layout diagram by taking into account the difficulty of handling items.

[0053] When collecting data, the collection unit can select collected data taking into account the deterioration status of the item. For example, the collection unit uses the generation AI to monitor the deterioration status of the item in real time and prioritize collecting data on items that are deteriorating. The collection unit can also use the generation AI to predict the rate of deterioration of the item and prioritize collecting data on items that are deteriorating quickly. Furthermore, the collection unit can collect data on items that are less deteriorated based on the deterioration status of the item using the generation AI, and optimize long-term storage methods. For example, the collection unit evaluates the deterioration status based on the physical damage and expiration date of the item, and selects collected data based on the results. In this way, by selecting collected data taking into account the deterioration status of the item, it is possible to prioritize the management of items that are deteriorating.

[0054] When collecting data, the collection unit can select data to be collected by referring to the shipping history of the items. For example, the generation AI in the collection unit analyzes past shipping history and prioritizes the collection of data on items that are shipped frequently. The collection unit can also collect data on seasonal products by having the generation AI learn seasonal shipping patterns from the shipping history. Furthermore, the collection unit can also collect data that reflects the optimal arrangement and storage methods for specific customers based on the shipping history. For example, the collection unit selects data to be collected based on past shipping data and shipping frequency. In this way, by selecting data to be collected by referring to the shipping history of the items, the efficiency of shipping operations can be improved.

[0055] When collecting data, the collection unit can select the data to be collected by taking into consideration environmental data such as the temperature and humidity inside the warehouse. For example, the collection unit allows the generation AI to prioritize collecting data on items that are resistant to temperature changes based on temperature data inside the warehouse. The collection unit can also allow the generation AI to consider humidity data and focus on collecting data on items that are sensitive to humidity. Furthermore, the collection unit can allow the generation AI to combine temperature and humidity data and collect data on items that are optimal for the environmental conditions. For example, the collection unit selects the data to be collected based on temperature and humidity measurement data obtained by sensors. In this way, the quality of the items can be maintained by selecting the data to be collected by taking into consideration the environmental data inside the warehouse.

[0056] When collecting data, the collection unit can select the data to be collected by referring to the supplier information of the items. For example, the generation AI in the collection unit collects the optimal data for each supplier based on the supplier information of the items. The collection unit can also perform efficient data collection by having the generation AI analyze the shipping patterns of the supplier. Furthermore, the collection unit can also collect the optimal data for items from a specific supplier by having the generation AI take into account the characteristics of the supplier. For example, the collection unit selects the data to be collected based on data such as the supplier's name, location, and supply history. In this way, efficient storage is possible by selecting the data to be collected by referring to the supplier information of the items.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] During learning, the learning unit can optimize the learning algorithm by taking into account the deterioration status of items in the warehouse. For example, the generation AI can monitor the deterioration status of items in real time and prioritize learning on items that are deteriorating. The generation AI can also predict the rate at which items will deteriorate and apply a special learning algorithm to items that deteriorate quickly. Furthermore, the generation AI can prioritize learning on items that are less likely to deteriorate based on the deterioration status of the items, optimizing long-term storage methods. For example, the deterioration status can be evaluated based on the physical damage and expiration date of the items, and the learning algorithm can be optimized based on the results. In this way, by optimizing the learning algorithm by taking into account the deterioration status of the items, it is possible to prioritize managing items that are deteriorating.

[0059] During learning, the learning unit can refer to the shipping history of items to learn the optimal arrangement and storage methods. For example, the generation AI can analyze past shipping history and learn how to arrange frequently shipped items in an easy-to-reach location. The generation AI can also learn seasonal shipping patterns from the shipping history and suggest ways to efficiently arrange seasonal products. Furthermore, the generation AI can learn the optimal arrangement and storage methods for specific customers based on the shipping history and suggest customized arrangements. For example, it can learn the optimal arrangement and storage methods based on past shipping data and shipping frequency. This makes it possible to improve the efficiency of shipping operations by learning the optimal arrangement and storage methods by referring to the shipping history of items.

[0060] During learning, the learning unit can learn the optimal arrangement method based on the size and weight of the items. For example, the generation AI can take into account the size and weight of the items and learn how to arrange large, heavy items on the bottom shelves and small, light items on the top shelves. The generation AI can also learn an arrangement method that achieves efficient space utilization based on the size and weight of the items. Furthermore, the generation AI can learn an arrangement method that takes into account ease of retrieval and safety based on the size and weight of the items. For example, it can learn the optimal arrangement method based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by learning the optimal arrangement method based on the size and weight of the items.

[0061] During learning, the learning unit can adjust the learning algorithm by taking into account environmental data such as temperature or humidity within the warehouse. For example, the generation AI can learn how to arrange items that are resistant to temperature changes based on temperature data within the warehouse. The generation AI can also learn the optimal storage method for items that are sensitive to humidity by taking humidity data into account. Furthermore, the generation AI can combine temperature and humidity data to learn the optimal item arrangement method for environmental conditions. For example, the learning algorithm can be adjusted based on temperature and humidity measurement data from sensors. In this way, the quality of items can be maintained by adjusting the learning algorithm by taking into account environmental data within the warehouse.

[0062] During learning, the learning unit can refer to information about the supplier of an item to learn the optimal storage method. For example, the generation AI can learn the optimal storage method for each supplier based on the supplier information of the item. The generation AI can also analyze the shipping patterns of the supplier and learn efficient storage methods. Furthermore, the generation AI can take into account the characteristics of the supplier and propose the optimal storage method for items from a specific supplier. For example, it can learn the optimal storage method based on data such as the supplier's name, location, and supply history. This makes it possible to achieve efficient storage by learning the optimal storage method by referencing the supplier information of the item.

[0063] During learning, the learning unit can learn the optimal placement method, taking into account the expiration date of items. For example, the generation AI can learn how to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the item. The generation AI can also learn how to efficiently store items with a long expiration date, taking into account the expiration date. Furthermore, the generation AI can suggest the optimal placement method to prevent expiration based on the expiration date. For example, it can evaluate the expiration date based on the number of days since the date of manufacture and the results of quality inspections, and learn the optimal placement method based on the results. In this way, by learning the optimal placement method, taking into account the expiration date of items, it is possible to prevent expiration.

[0064] During optimization, the optimization unit can determine the optimal placement based on the shipping frequency of items. For example, the generation AI can analyze the shipping frequency of items and place items that are shipped frequently in locations where they are easy to access. It can also place items that are shipped less frequently at the back of the warehouse to efficiently use space. Furthermore, the generation AI can suggest placement to maximize the efficiency of shipping operations based on the shipping frequency. For example, it calculates the shipping frequency based on past shipping history and determines the optimal placement based on the results. In this way, the efficiency of shipping operations can be improved by determining the optimal placement based on the shipping frequency of items.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The learning unit learns how to arrange and store items in the warehouse. For example, the learning unit collects data such as the type, quantity, and location of items in the warehouse, which the generative AI analyzes. The learning unit can also learn information such as how specific items are arranged on which shelves, and which items are frequently shipped. Furthermore, the learning unit can use the generative AI to learn algorithms for optimizing how items are arranged and stored. For example, the generative AI can learn the optimal arrangement and storage methods based on past data. Step 2: The optimization unit optimizes the current layout of ordered and shipped products and warehouses based on the information learned by the learning unit. For example, the optimization unit places items that are shipped frequently in locations that are easy to access. The optimization unit can also suggest the optimal storage location when ordered products arrive. Furthermore, the optimization unit can use the generation AI to execute an algorithm to optimize the current layout of ordered and shipped products and warehouses. For example, the generation AI can suggest the optimal layout based on data on ordered and shipped products. Step 3: The placement generation unit generates a new placement plan based on the information optimized by the optimization unit if it determines that rearrangement is necessary. For example, the placement generation unit proposes the optimal placement of items that are in high demand during specific periods, such as seasonal products or campaign products. The placement generation unit can also rearrange items within the warehouse. Furthermore, the placement generation unit can use a generation AI to execute an algorithm for generating a new placement plan. For example, the generation AI generates a new placement plan based on data on items that require rearrangement.

[0067] (Example 2) An AI system according to an embodiment of the present invention learns how items are arranged and stored in a warehouse, and optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. The AI ​​system learns how items are arranged and stored in a warehouse, and optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. If it determines that rearrangement is necessary, it generates a new layout diagram. For example, the AI ​​system collects data such as the type, quantity, and location of items in the warehouse, and a generation AI analyzes this data. Next, the AI ​​system optimizes the daily shipping order and location of items based on the ordered and shipped items and the current warehouse layout. If it determines that rearrangement is necessary, the generation AI generates a new layout diagram. This allows the AI ​​system to optimize the shipping order and location of items in the warehouse, improving the efficiency of shipping operations. If rearrangement is necessary, the generation AI generates a new layout diagram, making it easier to manage items in the warehouse. This allows the AI ​​system to optimize the shipping order and location of items in the warehouse, improving the efficiency of shipping operations. For example, the time required for shipping operations is shortened, the burden on workers is reduced, and the risk of items being lost or shipped incorrectly is reduced.

[0068] The AI ​​system according to the embodiment includes a learning unit, an optimization unit, and an arrangement generation unit. The learning unit learns how to arrange and store items in a warehouse. For example, the learning unit collects data such as the type, quantity, and location of items in the warehouse, and the generation AI analyzes the data. The learning unit can also learn information such as how specific items are arranged on which shelves and which items are frequently shipped. Furthermore, the learning unit can use the generation AI to learn algorithms for optimizing how items are arranged and stored. For example, the generation AI learns optimal arrangements and storage methods based on past data. The optimization unit optimizes the current arrangement of ordered and shipped items and warehouse space based on the information learned by the learning unit. For example, the optimization unit arranges items that are frequently shipped in locations that make them easy to access. The optimization unit can also suggest optimal storage locations when ordered items arrive. Furthermore, the optimization unit can use the generation AI to execute algorithms for optimizing the current arrangement of ordered and shipped items and warehouse space. For example, the generation AI suggests optimal arrangements based on data on ordered and shipped items. The layout generation unit generates a new layout plan when it is determined that rearrangement is necessary based on the information optimized by the optimization unit. For example, the layout generation unit proposes an optimal layout for items that are in high demand during specific periods, such as seasonal products or campaign products. The layout generation unit can also rearrange items within a warehouse. Furthermore, the layout generation unit can execute an algorithm for generating a new layout plan using a generation AI. For example, the generation AI generates a new layout plan based on data on items that require rearrangement. As a result, the AI ​​system according to the embodiment can optimize the shipping order and positions of items within a warehouse and generate a new layout plan when rearrangement is necessary.

[0069] The AI ​​system includes a collection unit that collects data on the type or quantity of items in a warehouse and their locations. The collection unit collects data on the type, quantity, and locations of items in the warehouse. For example, the collection unit collects item data using barcode scanning. The collection unit can also collect item data automatically using sensors. Furthermore, the collection unit can manually input item data. For example, the collection unit provides an interface for a worker to manually input item data. In this way, the collection unit collects data on items in the warehouse, allowing the learning unit to learn based on more accurate information.

[0070] The optimization unit can place items that are shipped frequently in locations that are easy to retrieve. For example, the optimization unit identifies items that are shipped frequently based on past shipping history and places them in locations that are easy to retrieve. The optimization unit can also place items that are shipped frequently near the entrance of the warehouse. Furthermore, the optimization unit can place items that are shipped frequently on shelves that are easy to retrieve. For example, the optimization unit places items that are shipped frequently on shelves near the entrance of the warehouse, improving the efficiency of shipping operations. In this way, by placing items that are shipped frequently in locations that are easy to retrieve, the efficiency of shipping operations can be improved.

[0071] The optimization unit can propose the optimal storage location when the ordered items arrive. For example, when the ordered items arrive, the optimization unit proposes the optimal storage location based on the available space in the warehouse. The optimization unit can also propose the optimal storage location based on the type and size of the ordered items. Furthermore, the optimization unit can also propose the optimal storage location based on the shipping frequency of the ordered items. For example, if the shipping frequency of the ordered items is high, the optimization unit proposes storing the ordered items in a location that makes them easy to access. In this way, by proposing the optimal storage location when the ordered items arrive, it becomes easier to manage items in the warehouse.

[0072] The placement generation unit can propose an optimal placement for items that are in high demand during specific periods, such as seasonal products or campaign products. The placement generation unit proposes an optimal placement based on, for example, the sales period of seasonal products or campaign products. The placement generation unit can also propose an optimal placement based on the shipping history of items that are in high demand during specific periods. Furthermore, the placement generation unit can also propose an optimal placement based on the type and quantity of items that are in high demand during specific periods. For example, the placement generation unit proposes placing seasonal products in a location that makes them easy to access during their sales period. This allows for the efficient management of items in the warehouse by proposing an optimal placement for items that are in high demand during specific periods.

[0073] The placement generation unit can rearrange items in the warehouse. For example, the placement generation unit periodically reviews the placement of items in the warehouse and proposes an optimal placement. The placement generation unit can also rearrange items based on the shipping frequency and inventory status of the items. Furthermore, the placement generation unit can rearrange items in accordance with demand for seasonal products or campaign products. For example, the placement generation unit proposes rearranging items in the warehouse after the sales period for seasonal products has ended. In this way, rearranging items in the warehouse can improve the efficiency of item management.

[0074] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can have the generation AI select a simple data set, reducing the learning load. Furthermore, if the user is relaxed, the learning unit can have the generation AI select a detailed data set, improving the accuracy of learning. Furthermore, if the user is in a hurry, the learning unit can select a data set that the generation AI can learn quickly, shortening the learning time. For example, the learning unit can estimate the user's emotions using facial expression recognition technology and select training data based on the results. This allows for improved learning efficiency by selecting training data based on the user's emotions.

[0075] During learning, the learning unit can optimize the learning algorithm by taking into account the deterioration status of items in the warehouse. For example, the learning unit has the generation AI monitor the deterioration status of items in real time and prioritize learning on items that are deteriorating. The learning unit can also have the generation AI predict the rate at which items will deteriorate and apply a special learning algorithm to items that deteriorate quickly. Furthermore, the learning unit can prioritize learning on items that are less deteriorated based on the deterioration status of the items and optimize long-term storage methods. For example, the learning unit evaluates the deterioration status based on the physical damage and expiration date of the items and optimizes the learning algorithm based on the results. In this way, by optimizing the learning algorithm by taking into account the deterioration status of the items, it is possible to prioritize managing items that are deteriorating.

[0076] During learning, the learning unit can refer to the shipping history of items to learn the optimal arrangement and storage methods. For example, the generation AI analyzes past shipping history and learns how to arrange frequently shipped items in locations that make them easy to access. The learning unit can also have the generation AI learn seasonal shipping patterns from the shipping history and suggest ways to efficiently arrange seasonal products. Furthermore, the learning unit can have the generation AI learn the optimal arrangement and storage methods for specific customers based on the shipping history and suggest customized arrangements. For example, the learning unit learns the optimal arrangement and storage methods based on past shipping data and shipping frequency. This makes it possible to improve the efficiency of shipping operations by learning the optimal arrangement and storage methods by referring to the shipping history of items.

[0077] During learning, the learning unit can learn the optimal arrangement method based on the size and weight of the items. For example, the learning unit allows the generation AI to learn how to arrange large and heavy items on the bottom shelf and small and light items on the top shelf, taking into account the size and weight of the items. The learning unit can also learn an arrangement method that will enable the generation AI to achieve efficient space utilization based on the size and weight of the items. Furthermore, the learning unit can also allow the generation AI to learn an arrangement method that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the learning unit learns the optimal arrangement method based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by learning the optimal arrangement method based on the size and weight of the items.

[0078] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can cause the generation AI to set the learning frequency low, thereby reducing the user's burden. Furthermore, if the user is relaxed, the learning unit can cause the generation AI to set the learning frequency high, thereby improving the accuracy of learning. Furthermore, if the user is in a hurry, the learning unit can adjust the learning frequency to obtain results quickly. For example, the learning unit can estimate the user's emotions using facial expression recognition technology and adjust the learning frequency based on the results. This can improve learning efficiency by adjusting the learning frequency based on the user's emotions.

[0079] During learning, the learning unit can adjust the learning algorithm by taking into account environmental data such as temperature or humidity within the warehouse. For example, the generation AI of the learning unit learns how to arrange items that are resistant to temperature changes based on temperature data within the warehouse. The generation AI of the learning unit can also learn the optimal storage method for items that are sensitive to humidity by taking humidity data into account. Furthermore, the generation AI of the learning unit can combine temperature and humidity data to learn the optimal item arrangement method for environmental conditions. For example, the learning unit adjusts the learning algorithm based on temperature and humidity measurement data from sensors. In this way, the quality of items can be maintained by adjusting the learning algorithm by taking into account environmental data within the warehouse.

[0080] During learning, the learning unit can learn the optimal storage method by referring to information about the supplier of the item. For example, the learning unit uses the generation AI to learn the optimal storage method for each supplier based on information about the supplier of the item. The learning unit can also analyze the shipping patterns of the supplier and learn efficient storage methods. Furthermore, the learning unit can also propose the optimal storage method for items from a specific supplier by taking into account the characteristics of the supplier. For example, the learning unit learns the optimal storage method based on data such as the name, location, and supply history of the supplier. This makes it possible to learn the optimal storage method by referring to information about the supplier of the item.

[0081] During learning, the learning unit can learn the optimal placement method by taking into account the expiration date of the items. For example, the learning unit allows the generation AI to learn how to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the items. The learning unit can also allow the generation AI to learn how to efficiently store items with a long expiration date, based on the expiration date. Furthermore, the learning unit can also suggest the optimal placement method to prevent items from expiring, based on the expiration date. For example, the learning unit evaluates the expiration date based on the number of days elapsed since the date of manufacture and the results of quality inspections, and learns the optimal placement method based on the results. In this way, by learning the optimal placement method by taking into account the expiration date of the items, it is possible to prevent items from expiring.

[0082] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit causes the generation AI to relax the optimization criteria, thereby reducing the user's burden. Furthermore, if the user is relaxed, the optimization unit can also cause the generation AI to set stricter optimization criteria, thereby improving the accuracy of optimization. Furthermore, if the user is in a hurry, the optimization unit can adjust the optimization criteria to obtain results quickly. For example, the optimization unit can estimate the user's emotions using facial expression recognition technology and adjust the optimization criteria based on the results. This allows the optimization efficiency to be improved by adjusting the optimization criteria based on the user's emotions.

[0083] During optimization, the optimization unit can determine the optimal placement based on the shipping frequency of the items. For example, the generation AI analyzes the shipping frequency of the items and places frequently shipped items in locations where they are easy to access. The optimization unit can also place items with less shipping frequency at the back of the warehouse to efficiently utilize space. Furthermore, the optimization unit can suggest a placement that maximizes the efficiency of shipping operations based on the shipping frequency of the generation AI. For example, the optimization unit calculates the shipping frequency based on past shipping history and determines the optimal placement based on the result. In this way, the efficiency of shipping operations can be improved by determining the optimal placement based on the shipping frequency of the items.

[0084] During optimization, the optimization unit can determine the optimal placement by taking into account the difficulty of handling the items. For example, the optimization unit uses the generation AI to evaluate the difficulty of handling the items and place items that are difficult to handle in locations where they are easy to retrieve. The optimization unit can also place items that are easy to handle at the back of the warehouse to efficiently use space. Furthermore, the optimization unit can suggest placements that reduce the burden on workers based on the handling difficulty of the generation AI. For example, the optimization unit evaluates the handling difficulty based on the weight, shape, and effort required to handle the items, and determines the optimal placement based on the results. In this way, the burden on workers can be reduced by determining the optimal placement by taking into account the difficulty of handling the items.

[0085] During optimization, the optimization unit can determine the optimal placement by taking into account the storage conditions (temperature, humidity) of the items. For example, the optimization unit places items that are sensitive to temperature and humidity in the optimal location based on the storage conditions of the items using the generation AI. The optimization unit can also appropriately place items that require specific environmental conditions by taking into account the storage conditions. Furthermore, the optimization unit can also suggest the optimal placement for maintaining the quality of the items based on the storage conditions using the generation AI. For example, the optimization unit evaluates storage conditions based on the appropriate temperature range and humidity control standards, and determines the optimal placement based on the results. In this way, the quality of the items can be maintained by determining the optimal placement by taking into account the storage conditions of the items.

[0086] The optimization unit can estimate the user's emotions and determine the optimization priority based on the estimated user emotions. For example, if the user is feeling stressed, the optimization unit causes the generation AI to adjust the optimization priority to reduce the user's burden. Furthermore, if the user is relaxed, the optimization unit can also cause the generation AI to set stricter optimization priorities to improve optimization accuracy. Furthermore, if the user is in a hurry, the optimization unit can also adjust the optimization priority to obtain results quickly. For example, the optimization unit can estimate the user's emotions using facial expression recognition technology and determine the optimization priority based on the results. This can improve optimization efficiency by determining optimization priorities based on the user's emotions.

[0087] During optimization, the optimization unit can determine the optimal placement by taking into account information about the supplier of the item. For example, the generation AI of the optimization unit determines the optimal placement for each supplier based on information about the supplier of the item. The optimization unit can also analyze the shipping patterns of the supplier and determine an efficient placement. Furthermore, the optimization unit can propose the optimal placement for items from a specific supplier by taking into account the characteristics of the supplier through the generation AI. For example, the optimization unit determines the optimal placement based on data such as the name, location, and supply history of the supplier. This enables efficient storage by determining the optimal placement by taking into account information about the supplier of the item.

[0088] During optimization, the optimization unit can determine the optimal placement by taking into account the expiration date of the items. For example, the optimization unit uses the generation AI to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the items. The optimization unit can also store items with a long expiration date efficiently, based on the expiration date of the items. Furthermore, the optimization unit can propose the optimal placement to prevent items from expiring, based on the expiration date of the items. For example, the optimization unit evaluates the expiration date based on the number of days elapsed since the date of manufacture and the results of quality inspections, and determines the optimal placement based on the results. In this way, by determining the optimal placement by taking into account the expiration date of the items, it is possible to prevent items from expiring.

[0089] During optimization, the optimization unit can determine the optimal layout by taking into account the size and weight of the items. For example, the optimization unit may have the generation AI consider the size and weight of the items and place large, heavy items on the bottom shelves and small, light items on the top shelves. The optimization unit may also determine a layout that will achieve efficient space utilization based on the size and weight of the items. Furthermore, the optimization unit may suggest a layout that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the optimization unit may determine the optimal layout based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by determining the optimal layout by taking into account the size and weight of the items.

[0090] The layout generation unit can estimate the user's emotions and adjust the layout plan generation method based on the estimated user emotions. For example, if the user is feeling stressed, the layout generation unit causes the generation AI to generate a simple layout plan to make it easier to understand. Alternatively, if the user is relaxed, the layout generation unit can cause the generation AI to generate a detailed layout plan to improve accuracy. Furthermore, if the user is in a hurry, the layout generation unit can cause the generation AI to quickly generate a layout plan to save time. For example, the layout generation unit can estimate the user's emotions using facial expression recognition technology and adjust the layout plan generation method based on the results. In this way, by adjusting the layout plan generation method based on the user's emotions, it is possible to generate a layout plan that is easy to understand.

[0091] When generating a layout diagram, the layout generation unit can refer to the shipping history of the items to generate an optimal layout diagram. For example, the generation AI analyzes past shipping history and generates a layout diagram in which frequently shipped items are placed in locations where they are easy to access. The layout generation unit can also learn seasonal shipping patterns from the shipping history and generate a layout diagram in which seasonal products are efficiently placed. Furthermore, the generation AI can generate a layout diagram that reflects the optimal arrangement and storage method for a specific customer based on the shipping history. For example, the layout generation unit generates an optimal layout diagram based on past shipping data and shipping frequency. In this way, the efficiency of shipping work can be improved by generating an optimal layout diagram by referring to the shipping history of the items.

[0092] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram based on the size and weight of the items. For example, the layout generation unit generates a layout diagram in which the generation AI takes into account the size and weight of the items and places large, heavy items on the lower shelves and small, light items on the upper shelves. The layout generation unit can also generate a layout diagram that achieves efficient space utilization based on the size and weight of the items. Furthermore, the layout generation unit can generate a layout diagram that takes into account ease of retrieval and safety based on the size and weight of the items. For example, the layout generation unit generates an optimal layout diagram based on measurements of the dimensions and weight of the items. In this way, efficient space utilization can be achieved by generating an optimal layout diagram based on the size and weight of the items.

[0093] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the storage conditions (temperature, humidity) of the items. For example, the layout generation unit generates a layout diagram in which the generation AI places items that are sensitive to temperature and humidity in the optimal location based on the storage conditions of the items. The layout generation unit can also generate a layout diagram in which the generation AI takes storage conditions into account and appropriately places items that require specific environmental conditions. Furthermore, the layout generation unit can generate an optimal layout diagram for maintaining the quality of the items based on the storage conditions. For example, the layout generation unit evaluates storage conditions based on the appropriate temperature range and humidity control standards, and generates an optimal layout diagram based on the results. In this way, the quality of the items can be maintained by generating an optimal layout diagram by taking into account the storage conditions of the items.

[0094] The layout generation unit can estimate the user's emotions and adjust the layout diagram display method based on the estimated user emotions. For example, if the user is feeling stressed, the layout generation unit's generation AI can display a simple, highly visible layout diagram. Also, if the user is relaxed, the layout generation unit can display a layout diagram with detailed information. Furthermore, if the user is in a hurry, the layout generation unit can display a layout diagram that focuses on the main points. For example, the layout generation unit can estimate the user's emotions using facial expression recognition technology and adjust the layout diagram display method based on the results. In this way, by adjusting the layout diagram display method based on the user's emotions, a highly visible layout diagram can be displayed.

[0095] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account information about the supplier of the item. For example, the generation AI of the layout generation unit generates an optimal layout diagram for each supplier based on information about the supplier of the item. The layout generation unit can also generate an efficient layout diagram by analyzing the shipping patterns of the supplier. Furthermore, the layout generation unit can generate an optimal layout diagram for items from a specific supplier by taking into account the characteristics of the supplier. For example, the layout generation unit generates an optimal layout diagram based on data such as the name, location, and supply history of the supplier. This enables efficient storage by generating an optimal layout diagram by taking into account information about the supplier of the item.

[0096] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the expiration date of the items. For example, the layout generation unit generates a layout diagram in which the generation AI places items with upcoming expiration dates in locations where they are easy to access, based on the expiration dates of the items. The layout generation unit can also generate a layout diagram in which the generation AI takes expiration dates into account and efficiently stores items with longer expiration dates. Furthermore, the layout generation unit can generate an optimal layout diagram to prevent expiration dates based on the expiration dates. For example, the layout generation unit evaluates expiration dates based on the number of days elapsed since the date of manufacture and quality inspection results, and generates an optimal layout diagram based on the results. In this way, expiration dates can be prevented by generating an optimal layout diagram by taking expiration dates into account.

[0097] When generating a layout diagram, the layout generation unit can generate an optimal layout diagram by taking into account the difficulty of handling items. For example, the layout generation unit generates a layout diagram in which the generation AI evaluates the difficulty of handling items and places difficult-to-handle items in locations where they are easy to retrieve. The layout generation unit can also generate a layout diagram in which the generation AI places easy-to-handle items in the back of the warehouse to efficiently utilize space. Furthermore, the layout generation unit can generate a layout diagram that reduces the burden on workers based on the handling difficulty. For example, the layout generation unit evaluates the handling difficulty based on the weight, shape, and handling effort of the item, and generates an optimal layout diagram based on the results. In this way, the burden on workers can be reduced by generating an optimal layout diagram by taking into account the difficulty of handling items.

[0098] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can cause the generation AI to set the frequency of data collection low, thereby reducing the burden on the user. Furthermore, if the user is relaxed, the collection unit can cause the generation AI to set the frequency of data collection high, thereby improving the accuracy of the data. Furthermore, if the user is in a hurry, the collection unit can cause the generation AI to adjust the timing of data collection to obtain results quickly. For example, the collection unit can estimate the user's emotions using facial expression recognition technology and adjust the timing of data collection based on the results. This can reduce the burden on the user by adjusting the timing of data collection based on the user's emotions.

[0099] When collecting data, the collection unit can select collected data taking into account the deterioration status of the item. For example, the collection unit uses the generation AI to monitor the deterioration status of the item in real time and prioritize collecting data on items that are deteriorating. The collection unit can also use the generation AI to predict the rate of deterioration of the item and prioritize collecting data on items that are deteriorating quickly. Furthermore, the collection unit can collect data on items that are less deteriorated based on the deterioration status of the item using the generation AI, and optimize long-term storage methods. For example, the collection unit evaluates the deterioration status based on the physical damage and expiration date of the item, and selects collected data based on the results. In this way, by selecting collected data taking into account the deterioration status of the item, it is possible to prioritize the management of items that are deteriorating.

[0100] When collecting data, the collection unit can select data to be collected by referring to the shipping history of the items. For example, the generation AI in the collection unit analyzes past shipping history and prioritizes the collection of data on items that are shipped frequently. The collection unit can also collect data on seasonal products by having the generation AI learn seasonal shipping patterns from the shipping history. Furthermore, the collection unit can also collect data that reflects the optimal arrangement and storage methods for specific customers based on the shipping history. For example, the collection unit selects data to be collected based on past shipping data and shipping frequency. In this way, by selecting data to be collected by referring to the shipping history of the items, the efficiency of shipping operations can be improved.

[0101] The collection unit can estimate the user's emotions and prioritize the collected data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to adjust the priority of the collected data, thereby reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can cause the generation AI to strictly prioritize the collected data, thereby improving data accuracy. Furthermore, if the user is in a hurry, the collection unit can cause the generation AI to adjust the priority of the collected data, thereby enabling results to be obtained quickly. For example, the collection unit can estimate the user's emotions using facial expression recognition technology and prioritize the collected data based on the results. This reduces the user's burden by prioritizing the collected data based on the user's emotions.

[0102] When collecting data, the collection unit can select the data to be collected by taking into consideration environmental data such as the temperature and humidity inside the warehouse. For example, the collection unit allows the generation AI to prioritize collecting data on items that are resistant to temperature changes based on temperature data inside the warehouse. The collection unit can also allow the generation AI to consider humidity data and focus on collecting data on items that are sensitive to humidity. Furthermore, the collection unit can allow the generation AI to combine temperature and humidity data and collect data on items that are optimal for the environmental conditions. For example, the collection unit selects the data to be collected based on temperature and humidity measurement data obtained by sensors. In this way, the quality of the items can be maintained by selecting the data to be collected by taking into consideration the environmental data inside the warehouse.

[0103] When collecting data, the collection unit can select the data to be collected by referring to the supplier information of the items. For example, the generation AI in the collection unit collects the optimal data for each supplier based on the supplier information of the items. The collection unit can also perform efficient data collection by having the generation AI analyze the shipping patterns of the supplier. Furthermore, the collection unit can also collect the optimal data for items from a specific supplier by having the generation AI take into account the characteristics of the supplier. For example, the collection unit selects the data to be collected based on data such as the supplier's name, location, and supply history. In this way, efficient storage is possible by selecting the data to be collected by referring to the supplier information of the items. === Hard Collateral 1-1 === For example, each of the multiple elements including the learning unit, optimization unit, placement generation unit, and collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the learning unit learns how items are arranged and stored in a warehouse using the camera 42 and microphone 38B of the smart device 14, and analyzes the data using the control unit 46A. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the ordered and shipped items and the current placement of the warehouse. The placement generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a new placement diagram. The collection unit collects item data using the camera 42 and sensors of the smart device 14, and analyzes the data using the control unit 46A. === Hard Collateral 1-2 === For example, each of the multiple elements including the learning unit, optimization unit, placement generation unit, and collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit learns how items are arranged and stored in a warehouse using the camera 42 and microphone 238 of the smart glasses 214, and analyzes the data using the control unit 46A. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the ordered and shipped items and the current placement of the warehouse. The placement generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a new placement diagram. The collection unit collects item data using the camera 42 and sensors of the smart glasses 214, and analyzes the data using the control unit 46A. === Hard Collateral 1-3 === For example, each of the multiple elements including the learning unit, optimization unit, placement generation unit, and collection unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the learning unit learns how items are arranged and stored in a warehouse using the camera 42 and microphone 238 of the headset terminal 314, and analyzes the data using the control unit 46A. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the ordered and shipped items and the current placement of the warehouse. The placement generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a new placement diagram. The collection unit collects item data using the camera 42 and sensors of the headset terminal 314, and analyzes the data using the control unit 46A. === Hard Collateral 1-4 === For example, each of the multiple elements including the learning unit, optimization unit, placement generation unit, and collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the learning unit learns how to arrange and store items in a warehouse using the camera 42 and microphone 238 of the robot 414, and analyzes the data using the control unit 46A. The optimization unit is realized by the specific processing unit 290 of the data processing device 12, and optimizes the ordered / shipped items and the current placement of the warehouse. The placement generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates a new placement diagram. The collection unit collects data on items using the camera 42 and sensors of the robot 414, and analyzes the data using the control unit 46A.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can select a simple dataset to reduce the learning load. Alternatively, if the user is relaxed, the generation AI can select a detailed dataset to improve learning accuracy. Furthermore, if the user is in a hurry, the generation AI can select a dataset that can be learned quickly to shorten the learning time. For example, the learning unit can estimate the user's emotions using facial expression recognition technology and select training data based on the results. This allows for improved learning efficiency by selecting training data based on the user's emotions.

[0106] During learning, the learning unit can optimize the learning algorithm by taking into account the deterioration status of items in the warehouse. For example, the generation AI can monitor the deterioration status of items in real time and prioritize learning on items that are deteriorating. The generation AI can also predict the rate at which items will deteriorate and apply a special learning algorithm to items that deteriorate quickly. Furthermore, the generation AI can prioritize learning on items that are less likely to deteriorate based on the deterioration status of the items, optimizing long-term storage methods. For example, the deterioration status can be evaluated based on the physical damage and expiration date of the items, and the learning algorithm can be optimized based on the results. In this way, by optimizing the learning algorithm by taking into account the deterioration status of the items, it is possible to prioritize managing items that are deteriorating.

[0107] During learning, the learning unit can refer to the shipping history of items to learn the optimal arrangement and storage methods. For example, the generation AI can analyze past shipping history and learn how to arrange frequently shipped items in an easy-to-reach location. The generation AI can also learn seasonal shipping patterns from the shipping history and suggest ways to efficiently arrange seasonal products. Furthermore, the generation AI can learn the optimal arrangement and storage methods for specific customers based on the shipping history and suggest customized arrangements. For example, it can learn the optimal arrangement and storage methods based on past shipping data and shipping frequency. This makes it possible to improve the efficiency of shipping operations by learning the optimal arrangement and storage methods by referring to the shipping history of items.

[0108] During learning, the learning unit can learn the optimal arrangement method based on the size and weight of the items. For example, the generation AI can take into account the size and weight of the items and learn how to arrange large, heavy items on the bottom shelves and small, light items on the top shelves. The generation AI can also learn an arrangement method that achieves efficient space utilization based on the size and weight of the items. Furthermore, the generation AI can learn an arrangement method that takes into account ease of retrieval and safety based on the size and weight of the items. For example, it can learn the optimal arrangement method based on measuring the dimensions and weight of the items. In this way, efficient space utilization can be achieved by learning the optimal arrangement method based on the size and weight of the items.

[0109] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can set the learning frequency low to reduce the user's burden. Also, if the user is relaxed, the generation AI can set the learning frequency high to improve learning accuracy. Furthermore, if the user is in a hurry, the generation AI can adjust the learning frequency to obtain results quickly. For example, the user's emotions can be estimated using facial expression recognition technology and the learning frequency can be adjusted based on the results. In this way, adjusting the learning frequency based on the user's emotions can improve learning efficiency.

[0110] During learning, the learning unit can adjust the learning algorithm by taking into account environmental data such as temperature or humidity within the warehouse. For example, the generation AI can learn how to arrange items that are resistant to temperature changes based on temperature data within the warehouse. The generation AI can also learn the optimal storage method for items that are sensitive to humidity by taking humidity data into account. Furthermore, the generation AI can combine temperature and humidity data to learn the optimal item arrangement method for environmental conditions. For example, the learning algorithm can be adjusted based on temperature and humidity measurement data from sensors. In this way, the quality of items can be maintained by adjusting the learning algorithm by taking into account environmental data within the warehouse.

[0111] During learning, the learning unit can refer to information about the supplier of an item to learn the optimal storage method. For example, the generation AI can learn the optimal storage method for each supplier based on the supplier information of the item. The generation AI can also analyze the shipping patterns of the supplier and learn efficient storage methods. Furthermore, the generation AI can take into account the characteristics of the supplier and propose the optimal storage method for items from a specific supplier. For example, it can learn the optimal storage method based on data such as the supplier's name, location, and supply history. This makes it possible to achieve efficient storage by learning the optimal storage method by referencing the supplier information of the item.

[0112] During learning, the learning unit can learn the optimal placement method, taking into account the expiration date of items. For example, the generation AI can learn how to place items with an approaching expiration date in a location that makes them easy to access, based on the expiration date of the item. The generation AI can also learn how to efficiently store items with a long expiration date, taking into account the expiration date. Furthermore, the generation AI can suggest the optimal placement method to prevent expiration based on the expiration date. For example, it can evaluate the expiration date based on the number of days since the date of manufacture and the results of quality inspections, and learn the optimal placement method based on the results. In this way, by learning the optimal placement method, taking into account the expiration date of items, it is possible to prevent expiration.

[0113] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can relax the optimization criteria to reduce the user's burden. Alternatively, if the user is relaxed, the generation AI can set the optimization criteria to be stricter, improving the accuracy of the optimization. Furthermore, if the user is in a hurry, the generation AI can adjust the optimization criteria to obtain results quickly. For example, the optimization unit can estimate the user's emotions using facial expression recognition technology and adjust the optimization criteria based on the results. This makes it possible to improve the efficiency of optimization by adjusting the optimization criteria based on the user's emotions.

[0114] During optimization, the optimization unit can determine the optimal placement based on the shipping frequency of items. For example, the generation AI can analyze the shipping frequency of items and place items that are shipped frequently in locations where they are easy to access. It can also place items that are shipped less frequently at the back of the warehouse to efficiently use space. Furthermore, the generation AI can suggest placement to maximize the efficiency of shipping operations based on the shipping frequency. For example, it calculates the shipping frequency based on past shipping history and determines the optimal placement based on the results. In this way, the efficiency of shipping operations can be improved by determining the optimal placement based on the shipping frequency of items.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The learning unit learns how to arrange and store items in the warehouse. For example, the learning unit collects data such as the type, quantity, and location of items in the warehouse, which the generative AI analyzes. The learning unit can also learn information such as how specific items are arranged on which shelves, and which items are frequently shipped. Furthermore, the learning unit can use the generative AI to learn algorithms for optimizing how items are arranged and stored. For example, the generative AI can learn the optimal arrangement and storage methods based on past data. Step 2: The optimization unit optimizes the current layout of ordered and shipped products and warehouses based on the information learned by the learning unit. For example, the optimization unit places items that are shipped frequently in locations that are easy to access. The optimization unit can also suggest the optimal storage location when ordered products arrive. Furthermore, the optimization unit can use the generation AI to execute an algorithm to optimize the current layout of ordered and shipped products and warehouses. For example, the generation AI can suggest the optimal layout based on data on ordered and shipped products. Step 3: The placement generation unit generates a new placement plan based on the information optimized by the optimization unit if it determines that rearrangement is necessary. For example, the placement generation unit proposes the optimal placement of items that are in high demand during specific periods, such as seasonal products or campaign products. The placement generation unit can also rearrange items within the warehouse. Furthermore, the placement generation unit can use a generation AI to execute an algorithm for generating a new placement plan. For example, the generation AI generates a new placement plan based on data on items that require rearrangement.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a learning section for learning how to arrange or store items in a warehouse; an optimization unit that optimizes the current allocation of ordered and shipped products and warehouses based on the information learned by the learning unit; a layout generation unit that generates a new layout drawing when it is determined that rearrangement is necessary based on the information optimized by the optimization unit. A system characterized by:

2. A collection unit is provided for collecting data on the type or quantity of items in the warehouse and their locations.

2. The system of claim 1.

3. The optimization unit Place frequently shipped items in an easily accessible location 2. The system of claim 1.

4. The optimization unit Proposing the best storage location for ordered products when they arrive 2. The system of claim 1.

5. The placement generation unit Propose optimal placement for seasonal or promotional items that are in high demand during specific periods 2. The system of claim 1.

6. The placement generation unit Rearrange items in the warehouse 2. The system of claim 1.

7. The learning unit Estimate the user's emotions and select training data based on the estimated user emotions.

2. The system of claim 1.

8. The learning unit During learning, the learning algorithm is optimized by taking into account the deterioration status of items in the warehouse.

2. The system of claim 1.

9. The learning unit During the learning process, the system refers to the shipping history of items to learn the optimal arrangement and storage methods.

2. The system of claim 1.

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